Utilizing profile hidden Markov model databases for discovering viruses from metagenomic data: a comprehensive review.

Profile hidden Markov models (pHMMs) are able to achieve high sensitivity in remote homology search, making them popular choices for detecting novel or highly diverged viruses in metagenomic data. However, many existing pHMM databases have different design focuses, making it difficult for users to decide the proper one to use. In this review, we provide a thorough evaluation and comparison for multiple commonly used profile HMM databases for viral sequence discovery in metagenomic data. We characterized the databases by comparing their sizes, their taxonomic coverage, and the properties of their models using quantitative metrics. Subsequently, we assessed their performance in virus identification across multiple application scenarios, utilizing both simulated and real metagenomic data. We aim to offer researchers a thorough and critical assessment of the strengths and limitations of different databases. Furthermore, based on the experimental results obtained from the simulated and real metagenomic data, we provided practical suggestions for users to optimize their use of pHMM databases, thus enhancing the quality and reliability of their findings in the field of viral metagenomics.

Genomic and geographical structure of human cytomegalovirus.

Human cytomegalovirus (CMV) has infected humans since the origin of our species and currently infects most of the world's population. Variability between CMV genomes is the highest of any human herpesvirus, yet large portions of the genome are conserved. Here, we show that the genome encodes 74 regions of relatively high variability each with 2 to 8 alleles. We then identified two patterns in the CMV genome. Conserved parts of the genome and a minority (32) of variable regions show geographic population structure with evidence for African or European clustering, although hybrid strains are present. We find no evidence that geographic segregation has been driven by host immune pressure affecting known antigenic sites. Forty-two variable regions show no geographical structure, with similar allele distributions across different continental populations. These "nongeographical" regions are significantly enriched for genes encoding immunomodulatory functions suggesting a core functional importance. We hypothesize that at least two CMV founder populations account for the geographical differences that are largely seen in the conserved portions of the genome, although the timing of separation and direction of spread between the two are not clear. In contrast, the similar allele frequencies among 42 variable regions of the genome, irrespective of geographical origin, are indicative of a second evolutionary process, namely balancing selection that may preserve properties critical to CMV biological function. Given that genetic differences between CMVs are postulated to alter immunogenicity and potentially function, understanding these two evolutionary processes could contribute important information for the development of globally effective vaccines and the identification of novel drug targets.

False discovery rate control in genome-wide association studies with population structure.

We present a comprehensive statistical framework to analyze data from genome-wide association studies of polygenic traits, producing interpretable findings while controlling the false discovery rate. In contrast with standard approaches, our method can leverage sophisticated multivariate algorithms but makes no parametric assumptions about the unknown relation between genotypes and phenotype. Instead, we recognize that genotypes can be considered as a random sample from an appropriate model, encapsulating our knowledge of genetic inheritance and human populations. This allows the generation of imperfect copies (knockoffs) of these variables that serve as ideal negative controls, correcting for linkage disequilibrium and accounting for unknown population structure, which may be due to diverse ancestries or familial relatedness. The validity and effectiveness of our method are demonstrated by extensive simulations and by applications to the UK Biobank data. These analyses confirm our method is powerful relative to state-of-the-art alternatives, while comparisons with other studies validate most of our discoveries. Finally, fast software is made available for researchers to analyze Biobank-scale datasets.

Go Multivariate: Recommendations on Bayesian Multilevel Hidden Markov Models with Categorical Data.

The multilevel hidden Markov model (MHMM) is a promising method to investigate intense longitudinal data obtained within the social and behavioral sciences. The MHMM quantifies information on the latent dynamics of behavior over time. In addition, heterogeneity between individuals is accommodated with the inclusion of individual-specific random effects, facilitating the study of individual differences in dynamics. However, the performance of the MHMM has not been sufficiently explored. We performed an extensive simulation to assess the effect of the number of dependent variables (1-8), number of individuals (5-90), and number of observations per individual (100-1600) on the estimation performance of a Bayesian MHMM with categorical data including various levels of state distinctiveness and separation. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Moreover, including variables only consisting of random noise was generally not detrimental to model performance. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. However, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the level of state distinctiveness and separation and study objectives of the researcher.

Offspring thermal demands and parental brooding efficiency differ for precocial birds living in contrasting climates.

BACKGROUND: Chicks of precocial birds hatch well-developed and can search actively for food but their homeothermy develops gradually during growth. This makes them dependent on heat provided by parents ("brooding"), which is then traded off against other activities, mainly foraging. Although brooding has been documented in many precocial birds, little is known about the differences in the amount and efficiency of brooding care, brooding diel rhythmicity, and impact on the chick's growth, particularly between species living in different climatic conditions. RESULTS: We used multisensory dataloggers to evaluate brooding patterns in two congeneric species inhabiting contrasting climate zones: temperate Northern lapwing (Vanellus vanellus) and desert Red-wattled lapwing (Vanellus indicus). In accordance with our expectation, the adult desert lapwings brooded the chicks slightly less compared to the adult temperate lapwings. However, the desert lapwings brooded their chicks in higher ambient temperatures and less efficiently (i.e. they could not reach the same brooding temperature as the temperate lapwings), which are new and hitherto unknown brooding patterns in precocial birds. In both species, night brooding prevailed even during warm nights, suggesting a general brooding rule among birds. Although the high rates of brooding can reduce the time spent by foraging, we found no negative effect of the high brooding rate on the growth rate in either species. CONCLUSIONS: Our data suggest that the chicks of species breeding in colder climates may reduce their thermal demands, while their parents may increase the efficiency of parental brooding care. More research is however needed to confirm this as a rule across species.

Infinite hidden Markov models for multiple multivariate time series with missing data.

Exposure to air pollution is associated with increased morbidity and mortality. Recent technological advancements permit the collection of time-resolved personal exposure data. Such data are often incomplete with missing observations and exposures below the limit of detection, which limit their use in health effects studies. In this paper, we develop an infinite hidden Markov model for multiple asynchronous multivariate time series with missing data. Our model is designed to include covariates that can inform transitions among hidden states. We implement beam sampling, a combination of slice sampling and dynamic programming, to sample the hidden states, and a Bayesian multiple imputation algorithm to impute missing data. In simulation studies, our model excels in estimating hidden states and state-specific means and imputing observations that are missing at random or below the limit of detection. We validate our imputation approach on data from the Fort Collins Commuter Study. We show that the estimated hidden states improve imputations for data that are missing at random compared to existing approaches. In a case study of the Fort Collins Commuter Study, we describe the inferential gains obtained from our model including improved imputation of missing data and the ability to identify shared patterns in activity and exposure among repeated sampling days for individuals and among distinct individuals.

Combining mixed effects hidden Markov models with latent alternating recurrent event processes to model diurnal active-rest cycles.

Data collected from wearable devices can shed light on an individual's pattern of behavioral and circadian routine. Phone use can be modeled as alternating processes, between the state of active use and the state of being idle. Markov chains and alternating recurrent event models are commonly used to model state transitions in cases such as these, and the incorporation of random effects can be used to introduce diurnal effects. While state labels can be derived prior to modeling dynamics, this approach omits informative regression covariates that can influence state memberships. We instead propose an alternating recurrent event proportional hazards (PH) regression to model the transitions between latent states. We propose an expectation-maximization algorithm for imputing latent state labels and estimating parameters. We show that our E-step simplifies to the hidden Markov model (HMM) forward-backward algorithm, allowing us to recover an HMM with logistic regression transition probabilities. In addition, we show that PH modeling of discrete-time transitions implicitly penalizes the logistic regression likelihood and results in shrinkage estimators for the relative risk. This new estimator favors an extended stay in a state and is useful for modeling diurnal rhythms. We derive asymptotic distributions for our parameter estimates and compare our approach against competing methods through simulation as well as in a digital phenotyping study that followed smartphone use in a cohort of adolescents with mood disorders.

Boldness predicts plasticity in flight responses to winds.

Behavioural plasticity can allow populations to adjust to environmental change when genetic evolution is too slow to keep pace. However, its constraints are not well understood. Personality is known to shape individual behaviour, but its relationship to behavioural plasticity is unclear. We studied the relationship between boldness and behavioural plasticity in response to wind conditions in wandering albatrosses (Diomedea exulans). We fitted multivariate hidden Markov models to an 11-year GPS dataset collected from 294 birds to examine whether the probability of transitioning between behavioural states (rest, prey search and travel) varied in response to wind, boldness and their interaction. We found that movement decisions varied with boldness, with bolder birds showing preferences for travel, and shyer birds showing preferences for search. For females, these effects depended on wind speed. In strong winds, which are optimal for movement, females increased time spent in travel, while in weaker winds, shyer individuals showed a slight preference for search, while bolder individuals maintained preference for travel. Our findings suggest that individual variation in behavioural plasticity may limit the capacity of bolder females to adjust to variable conditions and highlight the important role of behavioural plasticity in population responses to climate change.

Incorporating multidimensional behavior into a risk management tool for a critically endangered and migratory species.

Conservation of migratory species exhibiting wide-ranging and multidimensional behaviors is challenged by management efforts that only utilize horizontal movements or produce static spatial-temporal products. For the deep-diving, critically endangered eastern Pacific leatherback turtle, tools that predict where turtles have high risks of fisheries interactions are urgently needed to prevent further population decline. We incorporated horizontal-vertical movement model results with spatial-temporal kernel density estimates and threat data (gear-specific fishing) to develop monthly maps of spatial risk. Specifically, we applied multistate hidden Markov models to a biotelemetry data set (n = 28 leatherback tracks, 2004-2007). Tracks with dive information were used to characterize turtle behavior as belonging to 1 of 3 states (transiting, residential with mixed diving, and residential with deep diving). Recent fishing effort data from Global Fishing Watch were integrated with predicted behaviors and monthly space-use estimates to create maps of relative risk of turtle-fisheries interactions. Drifting (pelagic) longline fishing gear had the highest average monthly fishing effort in the study region, and risk indices showed this gear to also have the greatest potential for high-risk interactions with turtles in a residential, deep-diving behavioral state. Monthly relative risk surfaces for all gears and behaviors were added to South Pacific TurtleWatch (SPTW) (https://www.upwell.org/sptw), a dynamic management tool for this leatherback population. These modifications will refine SPTW's capability to provide important predictions of potential high-risk bycatch areas for turtles undertaking specific behaviors. Our results demonstrate how multidimensional movement data, spatial-temporal density estimates, and threat data can be used to create a unique conservation tool. These methods serve as a framework for incorporating behavior into similar tools for other aquatic, aerial, and terrestrial taxa with multidimensional movement behaviors.

Incorporación del comportamiento multidimensional a una herramienta de gestión de riesgos para una especie migratoria en peligro crítico Resumen La conservación de especies migratorias con comportamientos amplios y multidimensionales se enfrenta a los esfuerzos de gestión que sólo utilizan movimientos horizontales o que producen resultados espaciotemporales estáticos. La tortuga laúd, una especie de las profundidades en peligro crítico, necesita con urgencia herramientas que pronostiquen los lugares en donde las tortugas tienen mayor riesgo de interactuar con las pesquerías para prevenir una mayor declinación poblacional. Incorporamos los resultados de un modelo de movimiento horizontal-vertical a las estimaciones de la densidad del núcleo espaciotemporal y de los datos de amenaza (equipo de pesca específico) para desarrollar mapas mensuales del riesgo espacial. De manera más concreta, aplicamos modelos ocultos multiestado de Markov a un conjunto de datos de biotelemetría (n=28 rastros de tortugas laúd, 2004-2007). Usamos los rastros con información de inmersión para caracterizar el comportamiento de las tortugas como uno de tres estados: en tránsito, inmersión mixta o por residencia e inmersión profunda o por residencia. Integramos los datos recientes del esfuerzo de pesca tomados de Global Fishing Watch a los comportamientos pronosticados y las estimaciones del uso mensual del espacio para crear mapas del riesgo relativo de las interacciones tortuga-pesquería. La pesca con palangre de deriva (pelágica) tuvo el promedio mensual más alto de esfuerzo de pesca en la región de estudio. Los índices de riesgo indicaron que este equipo también tiene el potencial más elevado de interacciones de alto riesgo con las tortugas en estado residencial o de inmersión profunda. Añadimos los comportamientos y las superficies de riesgo relativo mensuales a South Pacific Turtle Watch (SPTW) (https://www.upwell.org/sptw), una herramienta dinámica para la gestión de esta población de laúdes. Estos cambios pulirán la capacidad de SPTW para proporcionar predicciones importantes de las áreas con potencial alto de riesgo de pesca accesoria para las tortugas con comportamientos específicos. Nuestros resultados demuestran cómo los datos de movimiento multidimensional, las estimaciones de densidad espaciotemporal y los datos de amenaza pueden ser usados para crear una herramienta única de conservación. Estos métodos sirven como marco para incorporar el comportamiento a herramientas similares para otros taxones acuáticos, aéreos y terrestres con comportamientos multidimensionales.

Nanocatalysts as fast and powerful medical intervention: Bridging cochlear implant therapies and advanced modelling using Hidden Markov Models (HMMs) for effective treatment of inner ear infections.

As human population growth and waste from technologically advanced industries threaten to destabilise our delicate ecological equilibrium, the global spotlight intensifies on environmental contamination and climate-related changes. These challenges extend beyond our external environment and have significant effects on our internal ecosystems. The inner ear, which is responsible for balance and auditory perception, is a prime example. When these sensory mechanisms are impaired, disorders such as deafness can develop. Traditional treatment methods, including systemic antibiotics, are frequently ineffective due to inadequate inner ear penetration. Conventional techniques for administering substances to the inner ear fail to obtain adequate concentrations as well. In this context, cochlear implants laden with nanocatalysts emerge as a promising strategy for the targeted treatment of inner ear infections. Coated with biocompatible nanoparticles containing specific nanocatalysts, these implants can degrade or neutralise contaminants linked to inner ear infections. This method enables the controlled release of nanocatalysts directly at the infection site, thereby maximising therapeutic efficacy and minimising adverse effects. In vivo and in vitro studies have demonstrated that these implants are effective at eliminating infections, reducing inflammation, and fostering tissue regeneration in the ear. This study investigates the application of hidden Markov models (HMMs) to nanocatalyst-loaded cochlear implants. The HMM is trained on surgical phases in order to accurately identify the various phases associated with implant utilisation. This facilitates the precision placement of surgical instruments within the ear, with a location accuracy between 91% and 95% and a standard deviation between 1% and 5% for both sites. In conclusion, nanocatalysts serve as potent medicinal instruments, bridging cochlear implant therapies and advanced modelling utilising hidden Markov models for the effective treatment of inner ear infections. Cochlear implants loaded with nanocatalysts offer a promising method to combat inner ear infections and enhance patient outcomes by addressing the limitations of conventional treatments.

Multimodal transcriptional control of pattern formation in embryonic development.

Predicting how interactions between transcription factors and regulatory DNA sequence dictate rates of transcription and, ultimately, drive developmental outcomes remains an open challenge in physical biology. Using stripe 2 of the even-skipped gene in Drosophila embryos as a case study, we dissect the regulatory forces underpinning a key step along the developmental decision-making cascade: the generation of cytoplasmic mRNA patterns via the control of transcription in individual cells. Using live imaging and computational approaches, we found that the transcriptional burst frequency is modulated across the stripe to control the mRNA production rate. However, we discovered that bursting alone cannot quantitatively recapitulate the formation of the stripe and that control of the window of time over which each nucleus transcribes even-skipped plays a critical role in stripe formation. Theoretical modeling revealed that these regulatory strategies (bursting and the time window) respond in different ways to input transcription factor concentrations, suggesting that the stripe is shaped by the interplay of 2 distinct underlying molecular processes.

Real-time threat assessment based on hidden Markov models.

An essential factor toward ensuring the security of individuals and critical infrastructures is the timely detection of potentially threatening situations. To this end, especially in the law enforcement context, the availability of effective and efficient threat assessment mechanisms for identifying and eventually preventing crime- and terrorism-related threatening situations is of utmost importance. Toward this direction, this work proposes a hidden Markov model-based threat assessment framework for effectively and efficiently assessing threats in specific situations, such as public events. Specifically, a probabilistic approach is adopted to estimate the threat level of a situation at each point in time. The proposed approach also permits the reflection of the dynamic evolution of a threat over time by considering that the estimation of the threat level at a given time is affected by past observations. This estimation of the dynamic evolution of the threat is very useful, since it can support the decisions by security personnel regarding the taking of precautionary measures in case the threat level seems to adopt an upward trajectory, even before it reaches the highest level. In addition, its probabilistic basis allows for taking into account noisy data. The applicability of the proposed framework is showcased in a use case that focuses on the identification of potential threats in public events on the basis of evidence obtained from the automatic visual analysis of the footage of surveillance cameras.

Human Activity Recognition with an HMM-Based Generative Model.

Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this work, we propose unsupervised, scaled, Dirichlet-based hidden Markov models to analyze human activities. Our motivation is that human activities have sequential patterns and hidden Markov models (HMMs) are some of the strongest statistical models used for modeling data with continuous flow. In this paper, we assume that emission probabilities in HMM follow a bounded-scaled Dirichlet distribution, which is a proper choice in modeling proportional data. To learn our model, we applied the variational inference approach. We used a publicly available dataset to evaluate the performance of our proposed model.

Semi-supervised adapted HMMs for P2P credit scoring systems with reject inference.

The majority of current credit-scoring models, used for loan approval processing, are generally built on the basis of the information from the accepted credit applicants whose ability to repay the loan is known. This situation generates what is called the selection bias, presented by a sample that is not representative of the population of applicants, since rejected applications are excluded. Thus, the impact on the eligibility of those models from a statistical and economic point of view. Especially for the models used in the peer-to-peer lending platforms, since their rejection rate is extremely high. The method of inferring rejected applicants information in the process of construction of the credit scoring models is known as reject inference. This study proposes a semi-supervised learning framework based on hidden Markov models (SSHMM), as a novel method of reject inference. Real data from the Lending Club platform, the most used online lending marketplace in the United States as well as the rest of the world, is used to experiment the effectiveness of our method over existing approaches. The results of this study clearly illustrate the proposed method's superiority, stability, and adaptability.

STRIDE: a command-line HMM-based identifier and sub-classifier of Plasmodium falciparum RIFIN and STEVOR variant surface antigen families.

BACKGROUND: RIFINs and STEVORs are variant surface antigens expressed by P. falciparum that play roles in severe malaria pathogenesis and immune evasion. These two highly diverse multigene families feature multiple paralogs, making their classification challenging using traditional bioinformatic methods. RESULTS: STRIDE (STevor and RIfin iDEntifier) is an HMM-based, command-line program that automates the identification and classification of RIFIN and STEVOR protein sequences in the malaria parasite Plasmodium falciparum. STRIDE is more sensitive in detecting RIFINs and STEVORs than available PFAM and TIGRFAM tools and reports RIFIN subtypes and the number of sequences with a FHEYDER amino acid motif, which has been associated with severe malaria pathogenesis. CONCLUSIONS: STRIDE will be beneficial to malaria research groups analyzing genome sequences and transcripts of clinical field isolates, providing insight into parasite biology and virulence.

Evaluation of vicinity-based hidden Markov models for genotype imputation.

BACKGROUND: The decreasing cost of DNA sequencing has led to a great increase in our knowledge about genetic variation. While population-scale projects bring important insight into genotype-phenotype relationships, the cost of performing whole-genome sequencing on large samples is still prohibitive. In-silico genotype imputation coupled with genotyping-by-arrays is a cost-effective and accurate alternative for genotyping of common and uncommon variants. Imputation methods compare the genotypes of the typed variants with the large population-specific reference panels and estimate the genotypes of untyped variants by making use of the linkage disequilibrium patterns. Most accurate imputation methods are based on the Li-Stephens hidden Markov model, HMM, that treats the sequence of each chromosome as a mosaic of the haplotypes from the reference panel. RESULTS: Here we assess the accuracy of vicinity-based HMMs, where each untyped variant is imputed using the typed variants in a small window around itself (as small as 1 centimorgan). Locality-based imputation is used recently by machine learning-based genotype imputation approaches. We assess how the parameters of the vicinity-based HMMs impact the imputation accuracy in a comprehensive set of benchmarks and show that vicinity-based HMMs can accurately impute common and uncommon variants. CONCLUSIONS: Our results indicate that locality-based imputation models can be effectively used for genotype imputation. The parameter settings that we identified can be used in future methods and vicinity-based HMMs can be used for re-structuring and parallelizing new imputation methods. The source code for the vicinity-based HMM implementations is publicly available at https://github.com/harmancilab/LoHaMMer .

The hidden brain-state dynamics of tACS aftereffects.

Non-invasive techniques to electrically stimulate the brain such as transcranial direct and alternating current stimulation (tDCS/tACS) are increasingly used in human neuroscience and offer potential new avenues to treat brain disorders. Previous research has shown that stimulation effects may depend on brain-states. However, this work mostly focused on experimentally induced brain-states over the course of several minutes. Besides such global, long-term changes in brain-states, previous research suggests, that the brain is likely to spontaneously alternate between states in sub-second ranges, which is much closer to the time scale at which it is generally believed to operate. Here, we utilized Hidden Markov Models (HMM) to decompose magnetoencephalography data obtained before and after tACS into spontaneous, transient brain-states with distinct spatial, spectral and connectivity profiles. Only one out of four spontaneous brain-states, likely reflecting default mode network activity, showed evidence for an effect of tACS on the power of spontaneous α-oscillations. The identified state appears to disproportionally drive the overall (non-state resolved) tACS effect. No or only marginal effects were found in the remaining states. We found no evidence that tACS influenced the time spent in each state. Although stimulation was applied continuously, our results indicate that spontaneous brain-states and their underlying functional networks differ in their susceptibility to tACS. Global stimulation aftereffects may be disproportionally driven by distinct time periods during which the susceptible state is active. Our results may pave the ground for future work to understand which features make a specific brain-state susceptible to electrical stimulation.

Evolution of a Cytoplasmic Determinant: Evidence for the Biochemical Basis of Functional Evolution of the Novel Germ Line Regulator Oskar.

Germ line specification is essential in sexually reproducing organisms. Despite their critical role, the evolutionary history of the genes that specify animal germ cells is heterogeneous and dynamic. In many insects, the gene oskar is required for the specification of the germ line. However, the germ line role of oskar is thought to be a derived role resulting from co-option from an ancestral somatic role. To address how evolutionary changes in protein sequence could have led to changes in the function of Oskar protein that enabled it to regulate germ line specification, we searched for oskar orthologs in 1,565 publicly available insect genomic and transcriptomic data sets. The earliest-diverging lineage in which we identified an oskar ortholog was the order Zygentoma (silverfish and firebrats), suggesting that oskar originated before the origin of winged insects. We noted some order-specific trends in oskar sequence evolution, including whole gene duplications, clade-specific losses, and rapid divergence. An alignment of all known 379 Oskar sequences revealed new highly conserved residues as candidates that promote dimerization of the LOTUS domain. Moreover, we identified regions of the OSK domain with conserved predicted RNA binding potential. Furthermore, we show that despite a low overall amino acid conservation, the LOTUS domain shows higher conservation of predicted secondary structure than the OSK domain. Finally, we suggest new key amino acids in the LOTUS domain that may be involved in the previously reported Oskar-Vasa physical interaction that is required for its germ line role.

Unveiling whole-brain dynamics in normal aging through Hidden Markov Models.

During normal aging, the brain undergoes structural and functional changes. Many studies applied static functional connectivity (FC) analysis on resting state functional magnetic resonance imaging (rs-fMRI) data showing a link between aging and the increase of between-networks connectivity. However, it has been demonstrated that FC is not static but varies over time. By employing the dynamic data-driven approach of Hidden Markov Models, this study aims to investigate how aging is related to specific characteristics of dynamic brain states. Rs-fMRI data of 88 subjects, equally distributed in young and old were analyzed. The best model resulted to be with six states, which we characterized not only in terms of FC and mean BOLD activation, but also uncertainty of the estimates. We found two states were mostly occupied by young subjects, whereas three other states by old subjects. A graph-based analysis revealed a decrease in strength with the increase of age, and an overall more integrated topology of states occupied by old subjects. Indeed, while young subjects tend to cycle in a loop of states characterized by a high segregation of the networks, old subjects' loops feature high integration, with a crucial intermediary role played by the dorsal attention network. These results suggest that the employed mathematical approach captures the complex and rich brain's dynamics underpinning the aging process.

Response Patterns to Weekly Short Message Service Health Surveys Among Diverse Youth at High Risk for Acquiring HIV.

HIV researchers use short messaging service (SMS)-based surveys to monitor health behaviors more closely than what would be possible with in-person assessment. Benefits are tempered by nonresponse to completing surveys. Understanding response patterns and their associated study participant characteristics would guide more tailored use of SMS-based surveys for HIV studies. We examined response to weekly 7-item SMS surveys administered as part of an HIV prevention trial. Using Mixture hidden Markov models (MHMM), we identified the underlying response patterns shared by subgroups of participants over time and quantified the association between these response patterns and participant characteristics. Three underlying response patterns were identified; responders, responders with phone-related errors, and non-responders. Non-responders versus responders were more likely to be younger, male, cis-gender, Black and Latinx participants with histories of homelessness, incarceration, and social support service utilization. Responders with phone-related errors compared to non-responders were more likely to be Black, Latinx, female, students, and have a history of incarceration and social support service utilization. More nuanced results from MHMM analyses better inform what strategies to use for increasing SMS response rates, including assisting in securing phone ownership/service for responders with phone-related errors and identifying alternative strategies for non-responders. Actively collecting and monitoring non-delivery notification data available from SMS gateway service companies offers another opportunity to identify and connect with participants when they are willing but unable to respond during follow-up.